M. Cantonati, R. Gerecke, I. Jüttner and E.J. Cox (Guest Editors) Springs: neglected key habitats for biodiversity conservation J. Limnol., 70(Suppl. 1): 106-121, 2011, DOI:10.3274/JL11-70-S1-08 Diversity and distribution of chironomids (Diptera, Chironomidae) in pristine Alpine and pre-Alpine springs (Northern Italy) Valeria LENCIONI*, Laura MARZIALI1) and Bruno ROSSARO2) Department of Invertebrate Zoology and Hydrobiology, Museo Tridentino di Scienze Naturali, Via Calepina 14, 38122 Trento, Italy 1) Water Research Institute CNR-IRSA, Via del Mulino 19, 20047 Brugherio (MB), Italy 2) Department of Agri-food and Urban Systems Protection, Università degli Studi di Milano, Via Celoria 2, 20133 Milano, Italy *e-mail corresponding author: [email protected] ABSTRACT The diversity and distribution of chironomids (Diptera, Chironomidae) were studied in relation to environmental factors in 81 springs under pristine conditions in the Italian Prealps and Alps (Trentino and Veneto, NE-Italy, 46°N, 10-11°E). Each spring was surveyed once, between May and November, in 2005 or in 2007-2008, within 50 m of the spring’s source (eucrenal). A total of 173 macroinvertebrate samples were collected, in which 26,871 chironomids (including larvae, pupae, pupal exuviae and adults) were counted. Five subfamilies (Tanypodinae, Diamesinae, Prodiamesinae, Orthocladiinae and Chironominae), 54 genera and 104 species/groups of species were identified. As expected, Orthocladiinae accounted for a large part of specimens (82%), followed by Diamesinae (10%), Chironominae Tanytarsini (6%) and Tanypodinae (2%). Together the Chironominae Chironomini and Prodiamesinae contributed less than 0.05% of the fauna. Larvae represented 97.5% of specimens, mostly juveniles (62.6%). Maximum richness and diversity occurred at intermediate altitudes (ca 900-2100 m a.s.l.). Most taxa were found in a small proportion of sites, and frequencies declined gradually for more widely distributed species. A high number (67%) of rare (= present in less than 10% of sites) taxa were found. Three to 27 taxa were identified per spring. The rheocrene/rheo-helocrene springs were richest in taxa (generally >15 taxa), the mineral spring was poorest, with only three taxa. Most taxa were crenophilous, including lentic, rheobiontic and bryophilous taxa. A Principal Component Analysis (PCA) was performed including 98 taxa. Axes were interpreted calculating the correlation coefficients between site scores and 24 environmental factors. The species with the highest scores were Pseudokiefferiella parva, Corynoneura sp. A, Metriocnemus eurynotus gr., Paratrichocladius skirwithensis and Tvetenia calvescens. Five clusters of sites were identified with K-means analysis on the basis of the first and second PCA axes and a Discriminant Analysis was used to detect environmental factors discriminating the clusters: altitude, canopy cover, hydrological regime, pH, and granulometry as percentage of cobbles and stones. The highly individual nature of springs was highlighted; within the same river basin, between springs and within a single spring. These results suggest that prudent and conservative land management should assume that all springs sheltering such unique faunal assemblages need protection. Key words: Orthocladiinae, biodiversity, eucrenal, spring types, south-eastern Alps 1. INTRODUCTION As in other freshwater habitats, chironomids dominate many freshwater springs, in abundance and species number (Lindegaard 1995; Gerecke et al. 1998; Stur & Wiedenbrug 2006). Nevertheless compared with other insects, chironomids from springs have been less intensively studied. This is mainly due to the difficulty in identifying larvae – in some genera even pupae and adults (Orendt 2000a), to species level. In fact, lists of midge species are rather uncommon in ecological studies and knowledge of the autecology, geonemy and phenology of spring-dwelling species is still fragmentary compared to other aquatic habitats (Orendt 2000b). The first works on spring fauna, focusing at least partly on chironomids, were the investigations by Bornhauser (1912), Nadig (1942), Zavřel & Pax (1951) and Thienemann (1954). Lindegaard (1995) gave a comprehensive review of the chironomid literature on springs listing 99 references, and discussed the main factors affecting midge distribution in springs. Over the last ten years, several papers focused on the invertebrate fauna of Alpine and pre-Alpine springs, and of spring-fed brooks (in Italy*): Crema et al. (1996)*, Bonettini & Cantonati (1998)*, Klein & Tockner (2000), Orendt (2000a), Rossaro et al. (2000)*, Füreder et al. (2001), Rossaro & Bettinetti (2001)*, Stur et al. (2002), Lencioni & Rossaro (2005)*, Sambugar et al. (2006)*, Stur & Wiedenbrug (2006), Lencioni (2007)* and Marziali et al. (2010)*. Up to 200 chironomid species are reported from cold European springs, and 73 from Italian Alpine springs, representing about 20% of the species recorded in Europe and Italy, respectively (Lindegaard 1995; Crema et al. 2006; Ferrarese 2006; Lencioni 2007). Nevertheless most crenal systems remain unexplored and no biotic indexes have yet been developed to determine their ecological status (Cantonati et al. 2006; Marziali et al. 2010). Springs and their organisms are good tools for monitoring changes in groundwater quality due to human impact. In particular, chironomids are the most useful indicators of the surface water quality, as well as the upper layer of groundwater, because the larvae are Journal of Limnology 70(Suppl. 1), 2011 Preprint copy 2 affected by organic content and heavy metal load in the sediments (Lafont & Durbec 1990). Within this context, two projects (CRENODAT and CESSPA) were recently financed by two Italian public administrations, the Autonomous Province of Trento and the Adige Basin Authority, both focused on springs and their fauna as tools for monitoring changes in groundwater quality due to human impact (water abstraction for potable or hydroelectric use, pesticide contamination, etc.). This work considers only a selected number of springs investigated within those frameworks, all natural and so considered pristine sites. The aims of this work were to: i) analyse chironomid taxa assemblages in natural springs of alpine and pre-alpine regions; ii) test whether springs can be separated into distinct groups according to chironomid fauna; iii) determine the main environmental factors structuring chironomid taxa assemblages in mountain springs. 2. METHODS 2.1. Study area A total number of 81 springs were investigated, located in the Italian Prealps (21) and Alps (60) (Autonomous Province of Trento and Veneto Region, NE-Italy, 46°N, 10-11°E). They lie in 5 siliceous and in 18 carbonate basins, within a wide altitudinal range (170-2792 m a.s.l.), and belong to 7 hydromorphological types: rheocrene, helocrene, limnocrene, hygropetric, rheo-helocrene, rheo-hygropetric, rheo-limnocrene (Tab. 1). One spring (PS1255 Fontane negre, Pale di San Martino) was mineral. Most springs were perennial, but 7 were intermittent (Cantonati et al. 2007): AD2314 Amola rock glacier, AD2739 Maroccaro rock glacier, AN430 Pozza 1 Lago Bagatol, CV1421 Tornante Slavarè, ML0580 Vajo del Croce, ML0950 Varalta, OC2792 Val di Pejo rock glacier. 2.2. Chironomid collection Each spring was surveyed once, between May and November, in 2005 (CRENODAT project) and in 20072008 (CESSPA project). Chironomid larvae and pupae were collected in the eucrenal zone (= within 50 m of the spring's source) of each spring. From one to three replicates were collected per spring by exploring different substratum typologies, depending on the spring morphology: a) coarse substratum (>0.2 cm, from gravel to stones); b) fine substratum (<0.2 cm, from sand to mud); c) submerged bryophytes. A pond net (100 µm mesh size) was used for 30 seconds in a) and b); 50 mL of surface sediment were also taken with a syringe (50 g) (b). 50 g of bryophytes were collected and washed in the laboratory to extract animals living within (c). Extra samples of larvae, pupae, pupal exuviae and adults were taken using tweezers, drift and sweep nets. Samples were preserved in 75% ethyl alcohol. V. Lencioni et al. Chironomids were mounted on slides and identified to species/groups of species according to Serra-Tosio (1971), Pinder (1978), Ferrarese & Rossaro (1981), Rossaro (1982), Ferrarese (1983), Wiederholm (1983, 1986, 1989), Nocentini (1985), Schmid (1993), Janecek (1998), Stur & Ekrem (2006), Lencioni et al. (2007a) and Rossaro et al. (2009). 2.3. Environmental variables During each biological survey, environmental variables were recorded, including those used for the landscape classification. Altitude was measured by GPS with an instrument error of about 10-15 m. The percent grain-size composition of the substratum was evaluated visually as percentage of gravel, cobbles, sand, silt, stones and rocks. Water samples were collected in acidcleaned graduated bottles for hydro-chemical analysis (conductivity, alkalinity, hardness, dissolved oxygen, % oxygen saturation, pH, nutrients, anions, cations, and metals). Analyses were performed using standard methods following the American Public Health Association (APHA, AWWA & WEF 2005). Water temperature was measured with a field multiprobe (Hydrolab). In addition, the canopy cover (shading) was visually estimated in five classes: 0%, 25%, 50%, 75%, and 100%. Discharge was measured using a graduated bucket, repeating the measurement in different areas of the spring. Average current velocity was measured with an OTT propeller-flow meter. Turbidity was recorded using a portable turbidimeter MicroTPI. More details are given in Cantonati et al. (2007). 2.4. Statistical analyses Only semi-quantitative samples collected in the three main substratum typologies were considered in the statistical analyses (drift and sweep net samples were not included); the mean abundance of each taxon per spring was considered. Rare species were included in the analysis, as recommended by Smith et al. (2001), resulting in a total of 98 taxa. Biological and environmental data were log (x+1) transformed, except for percentage values which were arcsine (square root) transformed. Shannon Diversity Index (Shannon & Weaver 1949) was calculated for each spring with the MVSP® 3.1 computer package. Pearson correlations between biological and environmental variables were calculated with STATISTICA® 8.0 computer package (StatSoft Inc. 2007). Values with p <0.05 were considered significant. Ordination analysis was carried out by means of the CANOCO® 4.5 computer package (Ter Braak & Šmilauer 2002). A Detrended Correspondence Analysis (DCA) was first run to detect whether the data had a unimodal or a linear structure according to the gradient length of axes. A gradient length between 2.5 and 4.4 suggested a linear model (Principal Component Analysis, PCA) as more appropriate (Ter Braak & Šmilauer 2002), and four axes were calculated. Chironomidae in mountain springs 3 Tab. 1. General characteristics of the 81 springs sampled within the CRENODAT and CESSPA Projects. Spring code: letters = Mt. group, numbers = altitude as m a.s.l. R = Rheocrene, HE = Helocrene, HY = Hygropetric, L = Limnocrene, RHE = RheoHelocrene, RHY = Rheo-Hygropetric, RL = Rheo-Limnocrene. Altitude is given as m a.s.l. Project Spring code Spring name CRENODAT CRENODAT CRENODAT CRENODAT CRENODAT CRENODAT CRENODAT CRENODAT CRENODAT CRENODAT CRENODAT CRENODAT CRENODAT CRENODAT CRENODAT CRENODAT CRENODAT CRENODAT CRENODAT CRENODAT CRENODAT CRENODAT CRENODAT CRENODAT CRENODAT CRENODAT CRENODAT CRENODAT CRENODAT CRENODAT CRENODAT CRENODAT CRENODAT CRENODAT CRENODAT CRENODAT CRENODAT CRENODAT CRENODAT CRENODAT CRENODAT CRENODAT CRENODAT CRENODAT CRENODAT CRENODAT CRENODAT CRENODAT CRENODAT CRENODAT CRENODAT CRENODAT CRENODAT CRENODAT CRENODAT CRENODAT CRENODAT CRENODAT CRENODAT CRENODAT CRENODAT CRENODAT CESSPA AD0905 AD1077 AD1235 AD1300 AD1665 AD1790 AD2314 AD2739 LD0584 LD0720 LD0928 LD0930 LD1400 LD1502 AN0430 AN1000 AN1474 AN1578 AN1950 BR0470 BR0658 BR0679 BR0790 BR0950 BR1315 BR1358 BR1379 BR1436 BR1605 BR1765 CS1350 CA1642 CA2153 VZ1178 MC1115 PS1255 PS1880 CV0250 CV0854 CV0962 CV0992 CV1084 CV1200 CV1280 CV1421 CV1433 CV1435 CV1575 CV1623 CV1655 CV1685 CV1855 CV2182 LT1240 MD1670 MD1871 OC2056 OC2792 PG0453 SL1724 AT0972 MB0335 MB0385 Vermongo bassa Frana edene Ponte Prese Borzago Ponte delle Cambiali Lago di Nambino Amola rock glacier Maroccaro rock glacier Fontanone Fiavè Del Graì Tof della glera alta Cortelì Tormendos Pozza 1 Vergnana Fondo Palu Longià Bordolona Maso Gori Faè 2 Tovare Sass Ross Acqua fredda Valagola Nambi Rislà 3 Scala di brenta Rivularia Corna Rossa Monzon Teleferica Brusà Bual del passetto Paul Poloni Fontane negre Salto busa dei Laibi V. Venegia Resenzuola palu' Giardini bassa Pian Gran Val tamburli Pirga Roncegno Perengola Val Calamento Telve Tornante Slavarè Acq. minerale. leggera Vetriolo Le mandre Torbiera di Grugola bassa Valmaggiore Busa delle rane Campigol dei solai Auzertol Stellune Daiano I ciei Monzon Fedaia Belvedere Val di Pejo rock glacier Trementina alta Antermont bassa Masere Diaol Gaon Region Mt. Group Alps Adamello Alps Adamello Alps Adamello Alps Adamello Alps Adamello Alps Adamello Alps Adamello Alps Adamello Alps Alpi di Ledro Alps Alpi di Ledro Alps Alpi di Ledro Alps Alpi di Ledro Alps Alpi di Ledro Alps Alpi di Ledro Alps Anauni Alps Anauni Alps Anauni Alps Anauni Alps Anauni Alps Brenta Alps Brenta Alps Brenta Alps Brenta Alps Brenta Alps Brenta Alps Brenta Alps Brenta Alps Brenta Alps Brenta Alps Brenta Alps Catinaccio Sassolungo Alps Cima d'Asta Alps Cima d'Asta Alps Cime Bocche Viezzena Alps Corno Mezzorona Alps Gruppo Pale Alps Gruppo Pale Alps Lagorai Alps Lagorai Alps Lagorai Alps Lagorai Alps Lagorai Alps Lagorai Alps Lagorai Alps Lagorai Alps Lagorai Alps Lagorai Alps Lagorai Alps Lagorai Alps Lagorai Alps Lagorai Alps Lagorai Alps Lagorai Alps Latemar Alps Marmolada Alps Marmolada Alps Ortles-Cevedale Alps Ortles-Cevedale Alps Paganella Gazza Alps Sella Prealps Altop Folgaria Tonezza Prealps Baldo Prealps Baldo Province Lithology Trento Trento Trento Trento Trento Trento Trento Trento Trento Trento Trento Trento Trento Trento Trento Trento Trento Trento Trento Trento Trento Trento Trento Trento Trento Trento Trento Trento Trento Trento Trento Trento Trento Trento Trento Trento Trento Trento Trento Trento Trento Trento Trento Trento Trento Trento Trento Trento Trento Trento Trento Trento Trento Trento Trento Trento Trento Trento Trento Trento Trento Trento Verona limestone limestone siliceous granite siliceous granite siliceous granite siliceous granite siliceous granite siliceous granite limestone limestone limestone limestone limestone limestone siliceous granite limestone limestone siliceous porphyry siliceous metamorphic limestone limestone limestone limestone limestone limestone limestone limestone limestone limestone limestone limestone siliceous metamorphic siliceous metamorphic limestone limestone limestone limestone limestone limestone siliceous porphyry limestone limestone siliceous porphyry siliceous metamorphic siliceous granite limestone siliceous porphyry siliceous granite siliceous porphyry siliceous granite siliceous porphyry siliceous porphyry siliceous porphyry limestone limestone limestone siliceous metamorphic siliceous granite limestone limestone limestone limestone limestone (continued) Altitude Type 905 1077 1235 1300 1665 1790 2314 2739 586 720 928 930 1400 1502 430 1000 1474 1578 1950 470 658 679 790 950 1315 1358 1379 1436 1605 1765 1350 1642 2153 1178 1115 1255 1880 250 854 962 992 1084 1200 1280 1421 1433 1435 1575 1623 1655 1685 1855 2182 1240 1670 1871 2056 2792 453 1724 972 335 385 R R R R R RHE R R R RHE R R R RHY R R RHE HE RHE R R R RHY R R R R HY RHY R RHE RHE R RL R RHY R R RHE R R R R R R HY R HE RHE R R R R R R R R R R L R R R 4 V. Lencioni et al. Tab. 1. Continuation. Project Spring code Spring name Region Mt. Group Province Lithology CESSPA CESSPA CRENODAT CRENODAT CRENODAT CRENODAT CRENODAT CRENODAT CESSPA CESSPA CESSPA CRENODAT CESSPA CESSPA CESSPA CRENODAT CRENODAT CRENODAT MB0445 MB0517 MB1440 BS0705 BS1527 BC0170 BC0503 BC0565 ML0533 ML0580 ML0950 MP0656 MP0676 MP0690 MP0924 MP1566 SC0250 VF0745 Prealba Lodrone Tolghe d Coel Viotte Lago Bagatol Madonina Val Lomasona Laurel Biasetti Vajo del Croce Varalta Vallarsa Biuchele Speccheri Cocher Fondo Comperlon Sette albi Ramon Freddo Fontanazzo Madonna del Sass Mezzano Prealps Prealps Prealps Prealps Prealps Prealps Prealps Prealps Prealps Prealps Prealps Prealps Prealps Prealps Prealps Prealps Prealps Prealps Baldo Baldo Baldo Bondone Stivo Bondone Stivo Brento Casale Brento Casale Brento Casale Lessini Lessini Lessini Pasubio Pasubio Pasubio Pasubio Pasubio Sette Comuni Vette Feltrine Verona Trento Trento Trento Trento Trento Trento Trento Verona Verona Verona Trento Trento Trento Trento Trento Trento Trento limestone limestone limestone limestone limestone limestone limestone limestone limestone limestone limestone limestone limestone limestone limestone limestone limestone limestone Altitude Type 445 517 1440 705 1527 170 503 565 533 580 950 656 676 690 924 1566 250 745 R R R R R R L R RHE L R R R RHE R HY R HY Fig. 1. Water temperature in relation to altitude. The resulting ordination axis scores were interpreted against 24 environmental factors (Appendix 1) by calculating Pearson's product-moment correlation coefficients. A K-means Cluster Analysis was carried out to cluster sites into similar groups based on chironomid taxon assemblages, considering site scores of the first two PCA axes as variables. K-means with five groups gave results of major ecological relevance, compared with K-means performed with 3, 4, 6 and 7 groups. A Discriminant Analysis based on the Wilk's lambda test was performed to detect the environmental factors separating the 5 K-means groups. Values with p <0.05 were considered significant. 3. RESULTS 3.1. Environmental features in the springs In table 2 the main physico-chemical and hydromorphological features of the 81 springs are shown. The study sites are rather heterogeneous, with wide ranges for all parameters. For example, water temperature ranged from 0.8 to 14.2 °C, % oxygen saturation from 35 to 105%, pH from 6.3 to 8.3, conductivity from 16 to 2120 µS cm-1, sulphate from 0.82 to 1368 mg L-1, nitrate nitrogen from 20 to 2853 µg L-1, total phosphorus from 1.8 to 73 µg L-1, orthophosphate from 0.8 to 48 µg L-1 and silica from 0.6 to 13 mg L-1. A strong correlation was found between altitude and water temperature (R2 = 0.63, p <0.01) (Fig. 1), the latter decreasing with increasing altitude. Generally, the lowest values of water temperature, pH, conductivity and nutrients were recorded at the springs located at highest altitudes, in siliceous basins of the Mt. Groups Adamello, OrtlesCevedale and Lagorai (Appendix 1). More details are given in Cantonati et al. (2007). 3.2. Chironomid diversity and distribution A total of 173 replicates of macroinvertebrates were collected, in which 45% (= 26,871 specimens including larvae, pupae, pupal exuviae and adults) were chironomids. Chironomidae in mountain springs 5 Tab. 2. List of chironomid taxa in the 81 springs investigated. Substratum and spring type preferences are reported in bold, variables significantly (p <0.05) correlated to a specific taxon. R= Rheocrene, HE= Helocrene, HY= Hygropetric, L= Limnocrene, RHE= Rheo-Helocrene, RHY= Rheo-Hygropetric, RL= Rheo-Limnocrene. *= species new to the Italian springs; ** = previously reported as Genus spp.; † species new to Italy. Empty cells when no association can be given (too low abundance or equal distribution of the taxon within microhabitats). ●: crenophilous-crenobiont taxa. Subfamily Species Species code Substratum preference Tanypodinae Nilotanypus dubius (Meigen, 1804)* Apsectrotanypus sp.* Psectrotanypus sp.* Krenopelopia sp. Macropelopia fittkaui Ferrarese & Ceretti, 1987 Macropelopia nebulosa (Meigen, 1804)* Macropelopia notata (Meigen, 1818) Natarsia sp.* Thienemannimyia sp.* Trissopelopia sp. Zavrelimyia sp. Diamesa aberrata Lundbeck, 1889 Diamesa cinerella Meigen, 1935* Diamesa dampfi gr. Diamesa incallida (Walker, 1856) Diamesa insignipes Kieffer, 1908 Diamesa latitarsis gr. Diamesa starmachi Kownacki & Kownacka, 1970* Diamesa steinboecki Goetghebuer, 1933* Diamesa tonsa (Walker, 1856)* Diamesa vaillanti Serra-Tosio, 1972* Potthastia gaedii (Meigen, 1838)* Pseudodiamesa branickii (Nowicki, 1873) Pseudokiefferiella parva (Edwards, 1932) Prodiamesa olivacea (Meigen, 1818) Acamptocladius reissi Cranston & Sæther, 1981 Brillia bifida (Kieffer, 1909 ) Brillia longifurca Kieffer, 1921* Bryophaenocladius spp.* Chaetocladius dentiforceps (Edwards, 1929)** Chaetocladius perennis (Meigen, 1830)** Chaetocladius piger gr.** Chaetocladius vitellinus gr.** Corynoneura lobata Edwards, 1924** Corynoneura scutellata Winnertz, 1846** Corynoneura sp.A** Cricotopus annulator Goetghebuer, 1927* Cricotopus fuscus (Kieffer, 1909) Cricotopus tremulus (Linnaeus, 1756)* Cricotopus trifascia Edwards, 1929* Diplocladius cultriger Kieffer, 1908* Eukiefferiella brehmi gr.* Eukiefferiella brevicalcar (Kieffer, 1911) Eukiefferiella claripennis Lundbeck, 1898 Eukiefferiella coerulescens (Kieffer, 1926)* Eukiefferiella cyanea Thienemann, 1936* Eukiefferiella devonica gr. Eukiefferiella gracei gr. Eukiefferiella minor (Edwards, 1929) Eukiefferiella similis Goetghebuer, 1939* Eukiefferiella tirolensis Goetghebuer, 1938* Heleniella serratosioi Ringe, 1976 Heterotanytarsus apicalis (Kieffer, 1921) Heterotrissocladius marcidus (Walker, 1956) Hydrobaenus sp.* Krenosmittia borealpina (Goetghebuer, 1944)** Limnophyes spp. Limnophyes asquamatus Søgaard Andersen, 1937* Metriocnemus fuscipes gr. Metriocnemus eurynotus gr. N_dubius Apsectrot Psectrot Krenopel M_fittkaui M_nebulosa M_notata Natarsia Thienema Trissope Zavrelim D_aberra D_cinere D_dampfi D_incall D_insign D_latita D_starma D_steinb D_tonsa D_vailla P_gaedii P_branic P_ parva P_olivac A_reissi B_bifida B_longif Bryophae C_dentif C_perenn C_piger C_vitell C_lobata C_scutel Cory_sp.A C_annula C_fuscus C_tremul C_trifas D_cultrig E_brehmi E_brevic E_clarip E_coerul E_cyanea E_devonic E_gracei E_minor E_simili E_tirole H_serrat H_apical H_marcid Hydrobae K_boreal Limnophy L_asquam M_fuscip M_euryno sand silt-mud sand ● ● ● Diamesinae ● ● ● ● ● ● ● ● ● Prodiamesinae Orthocladiinae ● ● ● ● ● ● ● ● ● ● ● ● Spring type preference HE HY silt-mud silt-mud cobbles/stones bryophytes bryophytes sand silt-mud, sand cobbles/stones bryophytes bryophytes cobbles/stones cobbles/stones cobbles/stones bryophytes cobbles/stones cobbles/stones cobbles/stones cobbles/stones bryophytes cobbles/stones silt-mud bryophytes gravel/cobbles bryophytes bryophytes cobbles/stones silt-mud cobbles/stones silt-mud gravel, bryophytes bryophytes bryophytes cobbles/stones cobbles/stones bryophytes silt-mud bryophytes, cobbles/stones silt-mud cobbles/stones cobbles/stones cobbles/stones sand stones/rock, bryophytes bryophytes bryophytes sand, silt silt-mud silt-mud bryophytes cobbles/stones bryophytes bryophytes bryophytes (continued) L, HE RHY L RHY HE R RHE R R R R R R RHE RL, HE RHE HY RHE R R, RL, HE R RHE R HY HY HY R RHY, HY RHE HE, RL R L HY 6 V. Lencioni et al. Tab. 2. Continuation. Subfamily Species ● ● ● ● ● ● ● ● ● ● ● Chironominae ● ● ● ● Species code Metriocnemus inopinatus Strenzke, 1950* M_inopina Metriocnemus terrester Pagast, 1941* M_terres Orthocladius spp. Orthocl Orthocladius (Euorthocladius) frigidus (Zetterstedt, 1838)E_frigidu Orthocladius (Eudactylocladius) fuscimanus Kieffer, 1908*E_fuscim Orthocladius (Euorthocladius) rivicola Kieffer, 1921 E_rivico Orthocladius (Symposiocladius) sp.* Symposio Parachaetocladius sp.* Parachae Paracricotopus niger (Kieffer, 1913)* Paracric Parakiefferiella gracillima (Kieffer, 1924) P_gracil Parametriocnemus boreolapinus Gouin, 1942 P_boreoa Parametriocnemus sp.A* Param_spA Parametriocnemus stylatus (Kieffer, 1924) P_stylatus Paraphaenocladius impensus (Walker, 1856)* P_impens Paratrichocladius rufiventris (Meigen, 1830) P_rufive Paratrichocladius skirwithensis (Edwards, 1929) P_skirwi Paratrissocladius excerptus (Walker, 1856)* P_excerp Parorthocladius nudipennis (Kieffer, 1908) P_nudipe Pseudorthocladius sp.* Pseudort Pseudosmittia sp.* Pseudosm Rheocricotopus chalybeatus (Edwards, 1929)* R_chalyb Rheocricotopus effusus (Walker, 1856) R_effusu Rheocricotopus fuscipes Kieffer, 1909* R_fuscip Stilocladius montanus Rossaro, 1979* S_montan Synorthocladius semivirens Kieffer, 1909* S_semivi Thienemannia gracilis Kieffer, 1909** T_gracili Thienemanniella clavicornis (Kieffer, 1911)** T_ clavic Thienemanniella vittata (Edwards, 1924)** T_vittat Tvetenia bavarica (Goetghebuer, 1934) T_bavari Tvetenia calvescens (Edwards, 1929) T_calves Tvetenia verralli (Edwards, 1929)* T_discol Paracladopelma sp.* Paraclad Krenopsectra fallax Reiss, 1969* Krenopse M_arista Micropsectra aristata Pinder, 1976* Micropsectra atrofasciata (Kieffer, 1911)* M_atrofa Micropsectra bavarica Stur & Ekrem, 2006* M_bavari Micropsectra schrankelae Stur & Ekrem, 2006* M_schran Micropsectra seguyi Casas & Laville, 1990* M_seguyi Micropsectra sofiae Stur & Ekrem, 2006* M_sofiae Micropsectra longicrista Stur & Ekrem, 2006** † M_longic Rheotanytarsus sp. Rheotany Stempellinella sp. Stempell Tanytarsus heusdensis Goetghebuer, 1923** T_heusde Tanytarsus pallidicornis (Walker, 1856)** T_pallid Five subfamilies (Tanypodinae, Diamesinae, Prodiamesinae, Orthocladiinae and Chironominae), 54 genera and 104 species/groups of species were identified (Tab. 2). Orthocladiinae accounted for 82% of the total chironomid fauna, followed by Diamesinae (10%), Chironominae Tanytarsini (6%) and Tanypodinae (2%). Together Chironominae Chironomini and Prodiamesinae contributed less than 0.05% of specimens. Larvae represented 97.5% of the animals, most of which were juveniles (62.6%). No significant correlation was found between taxon richness and Shannon Diversity Index with altitude, the highest values for both being associated with an intermediate altitudinal range (Figs 2, 3). Most taxa occurred at a small proportion of the sites, and frequencies declined gradually for more widely distributed species. A high number (68 = 67%) of rare (= present in less than Substratum preference Spring type preference bryophytes silt-mud, bryophytes bryophytes, cobbles/stones cobbles/stones, bryophytes cobbles/stones stones/rock cobbles/stones bryophytes, silt-mud bryophytes bryophytes silt-mud R silt-mud silt-mud cobbles/stones bryophytes, cobbles/stones silt-mud cobbles/stones cobbles/stones bryophytes cobbles/stones silt-mud bryophytes, cobbles/stones bryophytes, cobbles/stones cobbles/stones bryophytes, silt-mud bryophytes, cobbles/stones cobbles/stones bryophytes cobbles/stones, bryophytes bryophytes bryophytes, silt-mud silt-mud silt-mud, bryophytes bryophytes, cobbles/stones silt-mud cobbles/stones silt-mud silt-mud bryophytes, cobbles/stones silt-mud sand cobbles/stones silt-mud R RHY, HY R RHE RHE R R RL, RHE R R R RL, HY RL L RL RHY R, HY, RHY RHE RHE HE HY HY RL 10% of sites) taxa were found (Fig. 4). Of these, 35 (30%) occurred in only one site. These included several species of the genera Diamesa and Eukiefferiella, such as Diamesa cinerella and Diamesa tonsa (both only in AD1665 Ponte delle Cambiali), Diamesa incallida and Eukiefferiella tirolensis (in BR1358 Nambi), Diamesa insignipes (in OC2056 Belvedere), Diamesa latitarsis gr. (only in AD905 Vermongo Bassa), Diamesa starmachi (in MD1871 Fedaia), Eukiefferiella cyanea (in BR1436 Scala di Brenta), Eukiefferiella devonica gr. (in MP0676 Biuchele Speccheri), Eukiefferiella similis (in ML0533 Biasetti). Other very rare taxa were Paraphaenocladius impensus (in CV0250 Resenzuola Palù), Paratrissocladius excerptus (in AD905 Vermongo Bassa), Rheocricotopus chalybeatus (in MB0385 Gaon), Rheotanytarsus sp. (in AD1665 Ponte delle Cambiali) and Paracladopelma sp. (in AN1474 Fondo). Chironomidae in mountain springs 7 Fig. 2. Number of taxa per spring in relation to altitude (m a.s.l.). Fig. 3. Median, 25th, 75th percentile of Shannon Diversity Index in relation to altitude (m a.s.l.). Height of box (H)= 25th-75th percentile; whiskers = non-outlier values comprised within an interval of 1.5 x H. Fig. 4. Number of taxa in relation to number of sites occupied. 8 V. Lencioni et al. Fig. 5. Abundance-occupancy relationships. Three taxa were present in at least 50% of the sites (= common taxa): Tvetenia calvescens, Corynoneura sp. A and Metriocnemus eurynotus (= hygropetricus) gr. (Fig. 5). No taxon was present in all 81 sites. Widespread species generally tended to have a relatively higher abundance than those with restricted distributions. The local abundance of taxa increased (but not significantly, y = 0.396x - 0.541, R2 = 0.48) with the number of sites from which they were collected. The most frequent (= present in at least 50% of sites) and abundant (= mean density ≥29 ind./spring) taxa were Tvetenia calvescens and Corynoneura sp. A. Among the less common taxa, high abundance was observed for Orthocladius spp. (21% of the sites) and Paratrichocladius skirwithensis (36% of the sites) (Fig. 5). More than fifty taxa were new to Italian springs (Tab. 2) and one, Micropsectra longicrista, was new to Italy, found in the rheocrene Valagola (BR1315). In contrast, some previously recorded species (Ferrarese 2006) were not found, such as the ubiquist Chironominae Chironomus lacunarius (Wülker 1973), and the crenophilous Podonominae Paraboreochlus minutissimus (Strobl 1894). From 3 to 27 taxa were identified per spring. Only five springs hosted more than 20 taxa and more than 130 individuals. Three of these, all rheocrenes and at altitudes >1470 m a.s.l., are located in the Lagorai Mt. Group, in siliceous basins (porphyry) (CV1685 Campigol dei Solai, CV2182 Stellune, CV1855 Auzertol). The other two, rheo-helocrenes, are both located in the Anauni Mt. Group, one in a siliceous metamorphic basin (AN1950 Bordolona) and one on limestone (AN1474 Fondo). Three to 5 taxa were counted in 8 springs, distributed over seven different Mt. Groups, of different hydro-morphological types and located mainly at <1000 m a.s.l. (PS1255 Fontane Negre, rheohygropetric; PG0453 Trementina alta, rheocrene; ML0580 Vajo del Croce, limnocrene; MP0924 Fondo Comperlon, rheocrene; CV0992 Val Tamburli, rheocrene; BC0503 Madonnina Val Lomasona, limnocrene; MB0517 Lodrone, rheocrene; CV1433 Acqua minerale Vetriolo, hygropetric). The last was the only mineral (sulphurous) spring (SO42- = 409 mg L-1, conductivity = 1239 µS cm-1), in which the lowest richness was recorded (Limnophyes asquamatus, Bryophaenocladius spp. and Paratrichocladius skirwithensis). Diversity was higher in mixed-type springs, such as rheo-helocrenes, rheo-hygropetric and rheo-limnocrenes, but lowest in the limnocrenes. The widest range of diversity values was recorded in the rheocrenes (Fig. 6). 44% of specimens were found on the coarse substratum (>0.2 cm), 38% in submerged bryophytes and 18% in the finer sediment. Diversity was highest in coarse substratum (1.89), followed by fine substrate (1.82) and bryophytes (1.78). Thirty-eight taxa were common to all three substrate types. Sixteen taxa were exclusive to coarse substrata (Chaetocladius perennis, Corynoneura lobata, Cricotopus tremulus, Cricotopus trifascia, Diamesa incallida, Diamesa insignipes, Diamesa latitarsis gr., Diamesa tonsa, Diamesa vaillanti, Eukiefferiella coerulescens, Eukiefferiella cyanea, Macropelopia notata, Potthastia gaedii, Rheocricotopus chalybeatus, Orthocladius (Symposiocladius) sp. and Tanytarsus heusdensis). Thirteen taxa were exclusive to fine substrata: Corynoneura scutellata, Eukiefferiella brehmi gr., Eukiefferiella claripennis gr., Heterotanytarsus apicalis, Macropelopia fittkaui, Micropsectra bavarica, Parachaetocladius sp., Paraphaenocladius impensus, Paratrissocladius excerptus, Prodiamesa olivacea, Paracladopelma sp., Tanytarsus pallidicornis. Nine taxa were found only in bryophytes (Chaetocladius dentiforceps, Diplocladius cultriger, Eukiefferiella similis, Limnophyes asquamatus, Metriocnemus inopinatus, Natarsia sp., Parakiefferiella gracillima, Paracricotopus niger, Pseudosmittia sp.). No taxa were exclusive to helocrenes, limnocrenes and rheo-limnocrenes. Three taxa were captured only in Chironomidae in mountain springs 9 Fig. 6. Median, 25th, 75th percentile of Shannon Diversity Index in relation to spring types. Height of box (H)= 25th-75th percentile; whiskers = non-outlier values comprised within an interval of 1.5 x H. R= Rheocrene, RHE= Rheo-Helocrene, HE= Helocrene, L= Limnocrene, RHY= Rheo-Hygropetric, HY= Hygropetric, RL= Rheo-Limnocrene. hygropetric springs (Micropsectra bavarica, Eukiefferiella cyanea, Limnophyes asquamatus), nineteen in rheocrenes (Corynoneura scutellata, Cricotopus trifascia, Diamesa aberrata, D. incallida, D. insignipes, D. latitarsis gr., D. steinboecki, D. vaillanti, Eukiefferiella brehmi gr., E. devonica gr., Hydrobaenus sp., Metriocnemus inopinatus, Potthastia gaedii, Parakiefferiella gracillima, Paraphaenocladius impensus, Pseudorthocladius sp., Pseudosmittia sp., Rheocricotopus chalybeatus, Symposiocladius sp.), three in rheohelocrenes (Eukiefferiella similis, Paracricotopus niger, Paracladopelma sp.), and one in the rheo-hygropetric type (Macropelopia notata). Cold-stenothermal taxa (such as all Diamesa species) were restricted to stations located in siliceous basins at the highest altitudes (>1400 m a.s.l.), where the lowest temperatures, pH, conductivity and canopy cover (<50%) were recorded. 3.3. Chironomid community in relation to environmental factors Results of PCA, K-means Cluster Analysis and of Discriminant Analysis are given in tables 3, 4 and in figures 7, 8, 9. Four eigenvalues were selected by the Principal Component Analysis: 0.157, 0.085, 0.072 and 0.06 accounting for 37.4% of the total variance. Five clusters were identified on the basis of the chironomid taxon assemblages in the 81 springs (K-mean analysis) (Tab. 3, Fig. 7). The Discriminant Analysis selected 6 environmental variables as best associated with the observed chironomid assemblages (Tab. 4). A water temperature - altitude gradient was found along the first PCA axis (Fig. 8). Water temperature was positively correlated to conductivity, pH, high level of nutrients, canopy cover and limestone substratum, whereas altitude was correlated with current velocity, discharge, oxygen content, presence of bryophytes, coarse substratum. A canopy cover-lithology gradient was found along the second axis. Species-environment relationships accounted for 48.6% of total variance. Rheocrene and hygropetric springs grouped in the first (cluster D, E) and fourth (clusters A, E) quadrants, whereas most of helocrenes and rheo-helocrenes grouped in the second (cluster B) and third (cluster C) quadrants (Fig. 7). Limnocrene springs occurred in different clusters (one in cluster B, two in cluster C, and one rheo-limnocrene in cluster E). Sites richest in nutrient and organic debris were grouped in cluster B, with higher canopy cover that ensures shading and more allochtonous food. All the intermittent springs grouped in cluster C, and cold, high-altitude springs with less nutrients in cluster A. The taxa best associated with the site clusters were Paratrichocladius skirwithensis and Pseudokiefferiella parva (cluster A), Metriocnemus eurynotus (cluster D), Tvetenia calvescens and Corynoneura sp. A (cluster E) (Fig. 9). 4. DISCUSSION The percentage distribution of taxa within chironomid subfamilies was in accordance with previous studies (Lindegaard 1995; Stur et al. 2005; Stur & Wiedenbrug 2006), with Orthocladiinae as the most frequent, taxon-richest and abundant subfamily. Many cold stenothermal and rheophilous taxa were found, based on the prevalence of cold and rheocrene springs. Most larvae were juveniles, highlighting the role of stable crenal habitats, as nurseries, even for non-crenophilous species, and as a refuge against water current and abrupt environmental changes. 10 V. Lencioni et al. Tab. 3. K-means clusters of sites according to chironomid community. AD1665 AN1950 BR1765 CA2153 CV2182 MD1871 OC2056 PS1880 AD1790 AN1000 AN1578 AT0972 BC0565 BR0470 BR0790 BR0950 BS0705 BS1527 CS1350 CV0250 CV0854 CV0962 CV1084 LD0584 LD0720 LD0930 LD1400 LT1240 MB0335 MB1440 MC1115 MP0656 PG0453 SC0250 SL1724 VF0745 AD1077 AD2314 AD2739 AN0430 BC0170 PC Aaxis 1 PC Aaxis 2 K-means Cluster Distance -0.0768 0.9147 0.4515 0.4943 0.5422 0.5851 0.2026 0.0322 -0.293 -0.3259 -0.1638 -0.2594 0.0281 -0.224 -0.1976 -0.1464 -0.1987 -0.1897 0.0272 -0.0112 -0.2703 -0.1998 0.093 0.0377 -0.3717 -0.2155 -0.1672 -0.1353 -0.2753 -0.348 -0.2976 -0.2433 -0.1798 -0.1561 -0.0725 -0.3853 -0.1792 -0.2365 -0.4391 -0.3885 -0.3314 -0.6126 -0.5382 -0.5615 -0.7107 -0.7681 -0.5342 -0.3456 -0.5791 0.0321 0.0569 0.1931 0.2323 0.1927 0.0314 0.0943 0.4368 0.2395 0.0191 0.2032 0.1577 0.2479 0.4259 0.2619 0.2265 0.0826 0.2118 0.0811 0.4194 0.1508 0.1111 0.0383 0.2847 0.0403 0.0032 -0.0603 0.1361 -0.1235 -0.592 -0.2722 -0.0491 -0.1147 A A A A A A A A B B B B B B B B B B B B B B B B B B B B B B B B B B B B C C C C C 0.33 0.37 0.04 0.12 0.17 0.14 0.21 0.26 0.12 0.13 0.03 0.07 0.15 0.10 0.05 0.20 0.06 0.10 0.15 0.12 0.09 0.19 0.21 0.16 0.14 0.04 0.06 0.18 0.07 0.12 0.12 0.10 0.09 0.11 0.18 0.14 0.12 0.33 0.11 0.07 0.02 BC0503 BR0679 CA1642 CV0992 CV1433 CV1575 MB0385 MB0445 MB0517 MD1670 ML0533 ML0580 ML0950 MP0676 MP0690 MP0924 OC2792 PS1255 AD1235 AN1474 BR0658 BR1605 CV1280 CV1623 CV1855 LD1502 MP1566 AD0905 AD1300 BR1315 BR1358 BR1379 BR1436 CV1200 CV1421 CV1435 CV1655 CV1685 LD0928 VZ1178 PC Aaxis 1 PC Aaxis 2 K-means Cluster Distance -0.4723 -0.2583 -0.3433 -0.3437 -0.445 -0.086 -0.2961 -0.4692 -0.5076 -0.3156 -0.4608 -0.4812 -0.4565 -0.4097 -0.3527 -0.4551 -0.0599 -0.3197 0.7271 0.1475 0.5351 0.6548 0.227 0.2383 0.2769 0.5142 0.3253 0.3258 0.285 0.9106 0.2759 0.5455 0.238 0.5238 0.536 0.5177 0.6644 1.2396 0.1845 0.2105 -0.0498 -0.1505 -0.1889 -0.0568 -0.2752 -0.2014 -0.1073 -0.0699 -0.0455 -0.0656 -0.0305 -0.0699 -0.0804 -0.102 -0.0205 -0.1187 -0.3018 -0.1605 0.868 0.5622 0.3398 0.3387 0.3419 0.2728 0.2692 0.2618 0.5209 -0.014 0.0712 0.2003 -0.2281 -0.1691 0.0134 0.1428 -0.0281 -0.0904 -0.0601 0.0051 -0.1644 -0.1078 C C C C C C C C C C C C C C C C C C D D D D D D D D D E E E E E E E E E E E E E 0.11 0.07 0.03 0.06 0.12 0.19 0.05 0.10 0.13 0.06 0.11 0.10 0.09 0.05 0.09 0.07 0.24 0.03 0.39 0.21 0.11 0.19 0.14 0.16 0.14 0.14 0.09 0.12 0.17 0.34 0.21 0.10 0.19 0.13 0.03 0.04 0.12 0.53 0.24 0.21 Tab. 4. Discriminant Analysis results. Wilks' Lambda: 0.066 approx. F (96,21)=2.18, p <0.001. Environ. variable Wilks' Partial F-remove p-level Toler. 1-Toler. Altitude pH % cobbles % stones Canopy cover Regime 0.086 0.087 0082 0.087 0.086 0.081 0.772 0.756 0.806 0.763 0.771 0.811 3.907 4.284 3.195 4.112 3.939 3.081 0.007 0.004 0.020 0.006 0.007 0.024 0.229 0.173 0.334 0.385 0.444 0.363 0.771 0.827 0.666 0.615 0.556 0.637 Chironomidae in mountain springs 11 Fig. 7. Plot of springs in the plane of the first two PCA axes. Different colours and icons indicate different K-means clusters (A-E) of sites. Fig. 8. Plot of environmental variables best correlated to site scores (axes 1 and 2) of PCA based on the chironomid communities living in the 81 sampled springs. In bold, the six variables selected by the Discriminant Analysis separating the 5 K-means clusters of sites. This was also observed for the hyporheic zone of alpine streams, which are known to play a similar role for chironomids and other freshwater invertebrates (Lencioni et al. 2006). Taxon richness and diversity had their maxima at intermediate altitudes (between about 900 and 2100 m a.s.l.), as noted by other authors (Orendt 2000a). As expected, the rheo-helocrene springs were the most species rich, being a mosaic of different niches (Lindegaard 1995; Cantonati et al. 2006; Sambugar et al. 2006). Many captured taxa were crenophilous, as defined by Thienemann (1954), Lindegaard (1995), Stur et al. (2005), Stur & Wiedenbrug (2006) and Novikmec et al. (2007) (Tab. 2). Lindegaard (1995) only defined one of these species, Macropelopia fittkaui, as a true crenobiont. However, according to Stur & Wiedenbrug (2006), due to the difficulty of recognising true crenobiont species within the chironomids, it is preferable to consider crenophilous taxa and crenobionts together, as a single category. On the basis of our results, some species might be defined as lentic, rheobiontic (including madicoloushygropetric) and bryophilous, based on their occurrence in specific habitats/spring types (see Tab. 2). Our findings generally confirmed what was already known for such taxa, albeit with some exceptions. For example, Macropelopia notata was found associated with the rheo-hygropetric spring type, but was previously reported as typical of moss-rich helocrenes (Stur et al. 2005); Limnophyes asquamatus and Corynoneura scutellata occurred as madicolous and rheobionts respectively, not as ubiquists (Lindegaard 1995); Ortho- 12 V. Lencioni et al. Fig. 9. Plot of species in the first two PCA axes. Only the names of the taxa accounting for the largest quote of variance are reported. cladius (Eudactylocladius) fuscipes was not associated with bryophytes as previously reported (Lindegaard 1995; Stur & Wiedenbrug 2006); Paraphaenocladius impensus occurred as a rheobiont, but has been known as terrestrial/madicolous (Lindegaard 1995; Stur & Wiedenbrug 2006). More than 50% of species are new for Italian springs and one is new to Italy, highlighting the current poor knowledge of the fauna in Italian springs (Lencioni 2007). With respect to their the trophic role, Orthocladiinae (grazing organisms) were particularly common in bryophytes, Diamesinae were associated with coarse substrata, expected from their rheophilous habit. The predators or omnivores (Tanypodinae) were present in all microhabitat types, while the collectors (Tanytarsini, Prodiamesinae and Chironomini) were abundant in sediments. The relationships observed between the distribution and abundance of common and rare species suggests that the chironomid fauna cannot be considered nested, even if some level of nestedness was highlighted and a few hotspots of chironomid biodiversity were found (e.g., the rheocrene spring CV1685 Campigol dei Solai accounted for 27 taxa, including 100% of the most common species and 13% of the rarest). Most taxa were found at a small proportion of the sites and frequency categories declined gradually for more widely distributed species. This distribution pattern has also been observed for blackflies and other aquatic insects from montane freshwater systems (Malmqvist et al. 1999; Lencioni et al. 2007b). Species were distributed according to altitude, substratum composition, pH and canopy cover (and to those factors significantly correlated with them). These four factors were previously reported as determining the composition of macroinvertebrate assemblages (e.g., Glazier 1991; Smith et al. 2001; von Fumetti et al. 2006). However, as only 37.4% of the total variance was explained by the four principal canonical axes, other environmental factors may be important, such as competition and predation (Hahn 2000). No clear association with basin geology was highlighted, apart from the indirect link via positive correlations with pH and conductivity. As observed by other authors (e.g., Smith et al. 2001), a transition from one spring type to another appears to be gradual, and there is almost a continuum between "traditional" types (most types were distributed in all K-means clusters). Finally, as expected (Smith & Wood 2002; Smith et al. 2003; von Fumetti et al. 2006), intermittent springs, Chironomidae in mountain springs including three rock glaciers (AD2314 Amola, AD2739 Maroccaro, OC2792 Val di Pejo), hosted fewer taxa than the permanent ones, and none of the taxa was exclusive to such springs. In conclusion, the highly individual nature of the springs was evident; within the same river basin, within a spring and between different springs. This suggests that prudent and conservative land management should assume that all springs need protection to conserve their faunal assemblages. Deeper knowledge of species autecology is needed to assess and monitor the ecological status of springs. Therefore, studying the distribution and dynamics of the characteristic spring fauna will help to identify the most appropriate measures to mitigate adverse man-made effects on springs, to which, due to their small spatial extent, they are extremely vulnerable. ACKNOWLEDGEMENTS The authors thank all colleagues who participated in chironomid collection, including Daniele Fattori, Nicoletta Verdari, Nicola Angeli, Daniel Spitale, Massimiliano Tardio, Ermanno Bertuzzi. The research was carried out within two projects: 1) the CRENODAT Project ("Biodiversity assessment and integrity evaluation of springs of Trentino - Italian Alps - and long-term ecological research") financed by the Autonomous Province of Trento (Italy) (2004-2007) and coordinated by Marco Cantonati (Museo Tridentino di Scienze Naturali, Trento, Italy); 2) the CESSPA Project "Censimento e Studio delle Sorgenti e dei Pozzi del territorio alpino e prealpino di competenza dell'Autorità di Bacino del Fiume Adige" financed by the Authority of the Adige River (2007-2009) and coordinated by Leonardo Latella (Museo Civico di Storia Naturale, Verona, Italy). Part of data were included in the Master thesis by Gianni Sartori (University of Padua, Italy, 2008/2009). 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Chironomidae in mountain springs 15 A P P E N D I X 0.29 0.52 0.04 0.06 0.07 0.40 0.15 0.44 0.24 2.95 0.17 0.09 0.28 0.98 0.50 0.56 0.53 0.46 0.23 0.64 1.47 0.07 0.12 0.36 0.11 0.12 0.63 0.20 0.62 0.22 0.60 0.48 0.11 0.03 0.16 0.14 0.02 0.04 6.90 0.49 0.32 0.99 0.40 0.34 0.06 25.6 0.06 0.25 8.6 6.5 6.3 5.9 5.7 4.8 1.0 0.8 8.7 10.1 7.8 7.0 6.8 6.5 11.7 7.1 7.1 7.2 3.9 7.1 14.2 8.7 7.1 7.0 5.4 5.7 4.2 5.7 7.0 4.4 7.8 6.3 5.5 5.7 10.0 7.3 3.4 11.5 9.1 11.7 8.0 9.2 7.2 5.7 6.2 9.1 4.8 5.1 72 90 94 95 97 96 95 105 88 85 81 79 76 78 65 90 83 70 89 100 81 95 92 90 88 89 91 94 93 93 77 79 79 72 65 35 86 63 74 65 86 88 91 78 85 89 93 81 8 232 8 737 2 5.4 <dl 8 393 15 741 1 3.7 <dl 8 198 4 973 10 6.4 <dl 7 35 2 1224 3 6.1 6 7 29 1 354 1 4.6 <dl 7 28 2 297 3 8.0 <dl 6 19 1 1137 1 3.6 <dl 7 26 1 549 3 2.1 <dl 8 313 6 1690 1 2.1 <dl 8 392 10 768 3 7.1 <dl 8 354 3 815 2 1.9 <dl 8 229 2 597 2 4.1 <dl 8 271 7 1467 2 3.5 <dl 8 216 1 1117 2 1.9 <dl 8 473 20 1890 48 6.3 <dl 8 271 7 772 1 5.9 <dl 8 345 5 562 1 2.3 <dl 7 49 3 55 2 5.1 <dl 6 24 4 327 2 4.3 <dl 8 269 1 427 3 0.9 <dl 8 362 2 159 2 6.2 3 8 267 2 272 6 2.8 <dl 8 368 3 318 6 3.3 <dl 8 262 17 884 3 7.2 <dl 8 225 3 738 1 1.9 <dl 8 239 17 625 2 2.8 <dl 8 212 3 746 1 1.2 <dl 8 179 5 716 1 1.3 9 8 241 2 238 1 1.1 <dl 8 221 1 229 3 1.6 <dl 8 301 7 893 13 3.1 <dl 6 19 3 343 1 3.8 <dl 7 16 1 324 1 3.7 <dl 8 230 29 365 1 6.6 <dl 8 120 4 92 6 5.5 <dl 8 2120 1368 112 1 5.8 <dl 8 207 6 247 5 2.6 <dl 8 319 24 902 5 5.8 <dl 8 477 27 2853 2 6.3 <dl 7 44 3 108 2 12.5 <dl 8 299 4 1175 13 3.9 <dl 8 166 37 621 3 7.6 <dl 7 64 2 361 7 10.7 <dl 8 157 25 504 1 6.6 <dl 7 29 4 250 1 3.1 <dl 6 1239 409 20 11 10.7 14210 7 35 2 717 6 5.1 <dl 7 28 3 293 2 5.7 <dl (continued) Fe (µg L-1) 15 0 15 7.0 20 0 30 3.0 20 0 20 0.9 20 0 50 12.0 35 5 80 95.0 0 0 10 0.7 30 0 30 0.5 80 0 21 3.5 15 0 30 4.5 0 0 5 0.1 0 0 7 0.1 5 50 40 0.5 15 0 5 0.1 30 50 10 0.04 20 0 5 0.1 20 25 40 0.6 0 0 10 0.5 0 0 10 0.2 25 0 20 4.0 25 40 100 30.0 0 0 13 0.5 20 5 30 7.0 0 30 20 1.0 20 0 15 1.5 15 0 15 1.0 25 0 28 20.0 10 0 30 1.5 15 45 20 0.5 10 30 5 0.3 15 0 40 4.5 35 0 30 1.0 0 0 1 1.5 30 35 35 0.7 30 5 15 2.0 45 0 10 0.3 0 0 6 0.3 15 5 35 7.0 0 0 20 3.0 5 50 5 0.01 50 0 40 0.5 15 10 25 1.0 5 0 5 0.1 10 25 35 3.0 0 0 15 4.0 25 5 30 1.0 0 100 5 3.5 0 0 25 5.0 0 0 1 1.0 SiO2 (mg L-1) 15 10 10 10 5 30 15 0 10 50 15 0 10 0 35 5 20 40 10 0 10 0 30 15 10 5 0 0 10 0 0 40 0 15 10 50 0 10 0 10 0 15 5 10 0 0 10 40 P-PO4 (µg L-1) 30 20 25 5 10 40 15 5 15 30 20 5 10 5 5 10 55 30 10 5 50 5 10 15 20 15 0 0 5 5 0 10 0 5 20 30 0 40 0 5 0 10 5 10 0 0 50 40 N-NO3 (µg L-1) 20 35 20 35 30 0 30 10 45 0 45 10 40 10 40 20 5 0 50 25 10 35 10 20 40 30 70 20 30 60 20 0 30 20 5 0 80 10 45 30 20 60 45 20 30 0 25 0 SO42--(mg L-1) % stones 20 15 25 30 15 30 10 5 15 10 20 30 25 5 0 20 20 30 5 5 30 35 20 30 15 20 20 20 15 20 45 50 5 25 20 20 0 40 0 5 55 10 10 55 40 0 15 20 Cond. (µS cm-1) % silt 1 1 1 1 1 1 0 0 1 1 1 1 1 1 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 1 1 1 pH % sand 1 1 0 1 0 2 2 0 2 0 0 0 1 0 0 0 0 4 0 0 0 0 0 0 3 0 0 0 1 0 0 0 0 3 0 0 0 0 0 0 0 0 0 0 0 0 0 0 % O2 saturation % cobbles 3 6 5 7 6 6 7 0 3 9 4 5 0 7 3 0 9 5 9 8 4 8 9 9 6 4 9 6 5 8 5 9 7 7 9 7 1 3 9 3 7 0 3 6 6 9 8 7 Water Temp. (°C) % gravel 3 3 4 4 1 2 2 0 4 5 3 5 4 2 4 2 3 1 2 4 4 2 4 5 3 3 1 3 2 3 2 1 1 4 4 4 1 4 4 5 4 5 3 5 4 1 3 2 Turbidity (NTU) Regime Discharge (L s-1) Organic debris Velocity (cm s-1) Bryophytes 1 1 0 0 0 0 0 0 1 1 1 1 1 1 0 1 1 0 0 1 1 1 1 1 1 1 1 1 1 1 1 0 0 1 1 1 1 1 1 0 1 1 0 0 0 1 0 0 % rock Canopy cover AD0905 905 AD1077 1077 AD1235 1235 AD1300 1300 AD1665 1665 AD1790 1790 AD2314 2314 AD2739 2739 LD0584 586 LD0720 720 LD0928 928 LD0930 930 LD1400 1400 LD1502 1502 AN0430 430 AN1000 1000 AN1474 1474 AN1578 1578 AN1950 1950 BR0470 470 BR0658 658 BR0679 679 BR0790 790 BR0950 950 BR1315 1315 BR1358 1358 BR1379 1379 BR1436 1436 BR1605 1605 BR1765 1765 CS1350 1350 CA1642 1642 CA2153 2153 VZ1178 1178 MC1115 1115 PS1255 1255 PS1880 1880 CV0250 250 CV0854 854 CV0962 962 CV0992 992 CV1084 1084 CV1200 1200 CV1280 1280 CV1421 1421 CV1433 1433 CV1435 1435 CV1575 1575 Lithology Altitude (m a.s.l.) Spring code Appendix 1. Environmental variables included in the data analyses. The first five are dummy variables: Lithology= siliceous (0), limestone (1); Canopy cover= 0% (0), 0-25% (1), 25-50% (2), 50-75% (3), 75-100% (4); Bryophytes= 0, 1, 2….10 (quantitative); Organic debris= 0, 1, 2, 3, 4 (quantitative); Regime= permanent (1), intermittent (0). dl= detection level. 16 V. Lencioni et al. Bryophytes Organic debris Regime %gravel %cobbles %sand %silt %stones %crock Velocity (m s-1) Discharge (L s-1) Turbidity (NTU) Water Temp. (°C) % O2 saturation pH Cond. (µS cm-1) SO42--(mg L-1) N-NO3 (µg L-1) P-PO4 (µg L-1) SiO2 (mg L-1) 0 0 0 0 0 1 1 1 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 4 2 4 1 1 4 4 2 4 1 5 4 5 5 0 2 4 2 5 2 3 4 2 3 3 2 5 2 2 3 1 4 4 3 8 9 0 6 3 6 7 7 3 0 9 3 9 8 4 7 4 7 7 3 0 5 8 5 4 3 8 7 7 3 3 5 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 2 0 0 0 0 0 0 1 3 3 0 1 4 2 1 0 0 1 1 1 1 1 1 1 1 1 0 1 1 1 1 1 1 1 1 1 1 0 1 1 1 0 0 1 1 1 1 1 1 1 50 40 35 50 25 65 40 10 40 0 35 5 35 0 0 30 10 40 0 25 5 15 35 25 5 0 35 50 45 10 15 35 10 20 15 45 20 10 10 35 10 40 10 30 65 25 50 5 50 20 20 45 25 10 45 25 50 10 35 45 0 20 55 0 10 20 20 0 0 20 10 15 5 0 0 10 5 5 10 0 0 0 10 10 30 10 25 15 15 0 0 0 0 30 20 5 5 40 0 10 25 5 10 10 0 10 0 10 5 5 10 15 0 0 0 10 0 5 0 10 25 10 0 10 5 0 20 10 0 5 10 0 0 0 0 0 5 10 10 10 10 70 5 15 15 0 5 20 30 30 0 40 50 0 15 25 50 25 20 0 5 30 0 5 30 0 0 15 0 40 0 0 70 0 5 20 0 0 50 90 0 20 0 10 0 0 0 0 0 25 35 0 0 0 0 70 0 40 10 10 10 15 25 10 15 5 30 15 20 8 3 30 21 15 40 7 10 10 15 1 15 5 4 5 50 40 40 16 15 50 25 1.0 0.1 1.5 0.2 3.0 1.0 0.5 3.0 2.0 6.0 2.0 3.0 0.2 4.0 6.5 0.5 0.6 0.2 0.3 0.5 1.0 0.5 0.8 0.2 0.1 0.1 2.3 1.0 1.0 0.7 0.2 10.0 0.3 0.05 0.09 0.15 0.70 0.23 2.84 0.13 0.54 0.04 0.55 0.19 0.34 0.28 0.07 0.48 0.48 0.48 0.88 0.28 0.15 0.17 0.14 1.66 0.50 0.50 0.50 0.61 0.69 0.69 0.69 0.76 0.24 0.01 6.3 4.7 4.9 6.6 1.0 8.0 4.4 4.0 4.5 2.1 9.3 4.3 9.5 9.8 11.8 11.9 8.4 6.6 8.6 8.3 12.4 9.0 7.1 10.1 3.1 8.9 7.7 8.0 8.9 6.8 5.5 7.8 11.0 79 89 84 65 79 80 95 83 81 93 83 80 80 95 80 83 90 87 67 77 78 80 83 80 90 83 77 80 77 75 77 88 87 7 7 7 7 7 8 8 8 7 7 8 8 8 8 8 8 8 8 8 7 8 8 8 8 8 8 8 8 8 8 8 8 8 62 46 43 30 37 568 223 144 60 136 240 207 413 341 266 485 360 261 393 354 234 241 204 580 320 590 297 300 310 480 202 261 340 2 6 3 2 1 154 7 1 11 54 2 7 8 6 5 5 4 4 7 2 7 10 5 4 4 4 35 25 13 86 13 3 4 231 772 165 298 347 254 605 287 108 163 654 543 1230 1271 2257 2257 1129 1823 304 265 2094 333 803 1257 1257 1257 659 677 677 903 916 703 915 7 1 3 1 4 4 1 1 4 1 1 3 4 14 11 12 12 8 1 4 15 2 7 7 7 7 3 3 3 3 1 4 1 9.9 12 4.7 <dl 7.9 <dl 7.5 <dl 4.6 <dl 4.7 <dl 5.2 <dl 0.6 <dl 9.8 <dl 3.6 <dl 2.0 <dl 1.6 54 4.7 <dl 4.0 <dl 4 100 3.7 30 3.7 <dl 3.2 <dl 5.2 <dl 4.6 <dl 3.6 <dl 5.6 <dl 3.7 <dl 2.8 <dl 2.9 <dl 2.9 <dl 2.1 <dl 2.1 <dl 2.1 <dl 2.1 <dl 1.7 <dl 2.9 <dl 1.9 <dl Fe (µg L-1) Canopy cover CV1623 1623 CV1655 1655 CV1685 1685 CV1855 1855 CV2182 2182 LT1240 1240 MD16701670 MD18711871 OC2056 2056 OC2792 2792 PG0453 453 SL1724 1724 AT0972 972 MB0335 335 MB0385 385 MB0445 445 MB0517 517 MB1440 1440 BS0705 705 BS1527 1527 BC0170 170 BC0503 503 BC0565 565 ML0533 533 ML0580 580 ML0950 950 MP0656 656 MP0676 676 MP0690 690 MP0924 924 MP1566 1566 SC0250 250 VF0745 745 Lithology Altitude (m a.s.l.) Spring code Appendix 1. Continuation.
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